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An SVM-based high-quality article classifier for systematic reviews.

Seunghee Kim¹, Jinwook Choi¹

¹Department of Biomedical Engineering, College of Medicine, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul 110-799, Republic of Korea.

Journal of Biomedical Informatics

|November 2, 2013

Summary

This summary is machine-generated.

Support vector machine (SVM) classifiers trained with included and commonly excluded articles improve systematic review efficiency. This machine learning approach aids experts by reducing workload, especially when topic-specific data is limited.

Keywords:

Artificial intelligence Classification Evidence-based medicine Review literature as topic

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Area of Science:

Information Science
Biomedical Informatics
Machine Learning

Background:

Systematic reviews are crucial for evidence-based practice but are labor-intensive.
Automated tools can assist in screening large volumes of literature.

Purpose of the Study:

To evaluate the utility of Support Vector Machine (SVM)-based classifiers for article selection in systematic reviews.
To compare the effectiveness of training data sets comprising included/commonly excluded articles versus included/excluded articles.

Main Methods:

Developed and trained SVM classifiers using balanced data sets from 19 procedure and 4 drug systematic reviews.
Employed random sampling for data set balancing.
Utilized Area Under the Curve (AUC) as the primary evaluation metric.

Main Results:

Classifiers trained on included and commonly excluded articles demonstrated significantly higher AUCs.
This training strategy outperformed classifiers trained on included and excluded articles.

Conclusions:

Machine learning-based article classifiers can effectively reduce expert workload in systematic reviews.
Training SVM classifiers with a combination of included and commonly excluded articles is a superior strategy, particularly when topic-specific data is scarce.